Download A Course in In-Memory Data Management: The Inner Mechanics by Hasso Plattner PDF
By Hasso Plattner
Recent achievements in and software program improvement, akin to multi-core CPUs and DRAM capacities of a number of terabytes in line with server, enabled the creation of a innovative know-how: in-memory facts administration. This expertise helps the versatile and very quickly research of huge quantities of firm info. Professor Hasso Plattner and his learn team on the Hasso Plattner Institute in Potsdam, Germany, were investigating and instructing the corresponding strategies and their adoption within the software program for years.
This publication relies at the first on-line path at the openHPI e-learning platform, which used to be introduced in autumn 2012 with greater than 13,000 novices. The publication is designed for college students of laptop technological know-how, software program engineering, and IT similar matters. in spite of the fact that, it addresses enterprise specialists, selection makers, software program builders, know-how specialists, and IT analysts alike. Plattner and his team specialize in exploring the internal mechanics of a column-oriented dictionary-encoded in-memory database. lined themes contain - among others - actual info garage and entry, uncomplicated database operators, compression mechanisms, and parallel sign up for algorithms. past that, implications for destiny company functions and their improvement are mentioned. Readers are bring about comprehend the unconventional variations and merits of the recent expertise over conventional row-oriented disk-based databases.
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Extra info for A Course in In-Memory Data Management: The Inner Mechanics of In-Memory Databases
1007/978-3-642-36524-9_3, Ó Springer-Verlag Berlin Heidelberg 2013 15 16 3 Enterprise Application Characteristics Yet, research in current enterprise systems showed that this statement is not true [KGZP10, KKG+11]. The main difference between systems that handle these query types is that OLTP systems handle more queries with a single select or queries that are highly selective returning only a few tuples, whereas OLAP systems calculate aggregations for only a few columns of a table, but for a large number of tuples.
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Examples of structured data are results of the erythrogram, blood pressure, temperature measurements, or the patient’s gender. The in-memory technology enables the combination of both classes of patient data with additional external sources, such as clinical trials, pharmacological combinations or side-effects. As a result, physicians can prove their hypotheses by interactively combing data and reduce necessary manual and time-consuming searches. Physicians are able to access all relevant patient data and to take their decision on latest available patient details.